BACKGROUND: Low-grade gliomas (LGG) are notorious for their difficult early-stage diagnosis, limited treatment options, and poor prognosis, making them a focal point in cancer research. Long non-coding RNAs (lncRNAs) have been identified as regulators of metabolic reprogramming in tumor cells, offering new directions for LGG treatment. METHODS: This study employed data from the cancer genome atlas (TCGA), focusing on key fatty acid (FA) metabolismrelated lncRNA. A risk scoring model was developed using univariate/multifactorial and least absolute shrinkage and selection operator (LASSO) cox regression. Additionally, the study evaluated the role of these prognosticlncRNAs in LGG progression by assessing associations between LGG immune markers and tumor drug resistance.Finally, functional enrichment analysis highlighted the molecular roles of these lncRNAs. RESULTS: In this study, a total of 14prognostic lncRNAs were obtained.The risk model demonstrated excellent validity and reliability, making it a superior predictor of prognosis among patients with varying LGG risks. Among the identified lncRNAs, GHET-1 was notably associated with LGG sensitivity to current chemotherapy options and might be a crucial lncRNA affecting LGG progression.High-risk patients exhibited T-helper cell-mediated immunosuppression, potentially paving new paths for future LGG immunotherapy. CONCLUSION: Focusing on lncRNA regulation and FA metabolism reprogramming, this study established an innovative prognostic prediction model for LGG, showing outstanding validity and reliability. The findings offer new molecular and cellular targets for the future development of LGG treatments.